# DeepSeek-V4-Flash API Enters Public Beta With 1M Token Context

> The July 31 release upgrades agent performance on multiple benchmarks while maintaining the same model architecture as the preview and introducing native Responses API support at competitive per-token rates.

*Published 2026-07-31 · By Marcus Vance*

DeepSeek-V4-Flash is a Mixture-of-Experts language model with 284 billion total parameters and 13 billion active parameters released in public beta through the DeepSeek API on July 31, 2026.

The DeepSeek-V4-Flash API entered public beta on July 31, 2026, via the DeepSeek API platform. The model name deepseek-v4-flash was updated to DeepSeek-V4-Flash-0731 while preserving the identical architecture and parameter count from the preview version. Only re-post-training was applied to improve agent performance. This release focuses exclusively on the Flash API endpoint.

A 1 million token context window allows processing of entire large codebases or lengthy documents within a single interaction. The maximum output of 384,000 tokens supports generation of extended responses such as full application code or comprehensive analysis reports. These specifications target agent workflows that require sustained context across multiple steps.

## What technical specifications distinguish DeepSeek-V4-Flash from prior versions?

The model provides native support for the Responses API format along with tool calling and JSON output modes. It is also adapted for Codex, enabling direct use in coding agent environments. These features reduce the need for custom wrappers when integrating the model into existing agent frameworks.

The Mixture-of-Experts design activates only 13 billion parameters out of the total 284 billion during inference. This selective activation maintains computational efficiency while delivering high performance on specialized tasks. The approach contributes directly to the low per-token pricing offered in the public beta.

## How do the benchmark results demonstrate enhanced agent capabilities?

DeepSeek-V4-Flash recorded a score of 82.7 on Terminal Bench 2.1. This benchmark measures performance in terminal command and agent-driven task execution. The result indicates reliable handling of multi-step command sequences.

On the Toolathlon Verified benchmark the model achieved 70.3. This score reflects accuracy in verified tool invocation scenarios. Strong results here point to improved reliability when agents must select and execute external tools.

The DSBench-FullStack benchmark produced a score of 68.7. This evaluation covers full-stack development workflows. The outcome shows capability across integrated development tasks that combine frontend, backend, and deployment steps.

Benchmark performance of DeepSeek-V4-Flash reported in the official change logBenchmarkScoreFocus AreaTerminal Bench 2.182.7Terminal and command agent tasksToolathlon Verified70.3Verified tool use accuracyDSBench-FullStack68.7Full-stack development workflows

## What key features receive native support in the Responses API?

- Responses API format compatibility
- Tool calling functions
- JSON structured output
- Codex adaptation for coding agents
- 1M token context handling

## What pricing applies to the DeepSeek-V4-Flash API?

Input tokens are priced at $0.14 per million on cache miss. Output tokens are priced at $0.28 per million. The Responses API currently supports only the deepseek-v4-flash model under this pricing schedule.

## How does the release influence the frontier models market?

The combination of large context length, high output capacity, and low pricing intensifies competition on efficiency metrics. Developers building long-running agent systems gain access to extended context without proportional cost increases. The native tool calling and Responses API support lowers integration barriers for existing agent platforms.

Stakeholders in enterprise AI deployments may evaluate the model for tasks that previously required multiple shorter calls or external memory systems. The 13 billion active parameters deliver performance levels that challenge denser models at higher price points.

## What reactions accompanied the public beta announcement?

The official statement from DeepSeek highlighted upgrades to agent capabilities and benchmark improvements over the V4-Pro-Preview. Emphasis was placed on the native Responses API support and Codex adaptation as immediate integration benefits.

> 🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta! 🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. ... 🔷 The official V4-Flash now natively supports the Responses API format and is fully adapted for Codex!DeepSeek, Official account (@deepseek_ai)

## What developments are anticipated next from DeepSeek?

The official release of DeepSeek-V4-Pro will follow soon after the current Flash beta. The present update affects only the Flash API variant while Pro endpoints and web models stay unchanged. Further post-training refinements on the same architecture are expected to continue.

Developers monitoring the frontier models space will track whether the Pro version introduces additional parameter scaling or new feature sets while retaining the pricing and context advantages demonstrated in the Flash release.

## Sources

1. [The official release of the DeepSeek-V4-Flash API is now in public beta with benchmark results Terminal Bench 2.1: 82.7, Toolathlon verified: 70.3, DSBench-FullStack: 68.7, and native Responses API support.](https://api-docs.deepseek.com/updates/)
2. [CONTEXT LENGTH 1M, MAX OUTPUT MAXIMUM: 384K, PRICING 1M INPUT TOKENS (CACHE MISS) $0.14, 1M OUTPUT TOKENS $0.28, with Responses API and Tool Calls support.](https://api-docs.deepseek.com/quick_start/pricing/)
3. [DeepSeek announced the public beta release and benchmark improvements over V4-Pro-Preview along with Responses API and Codex support.](https://x.com/deepseek_ai/status/2083084415157022911)

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Source: https://aiintelreport.com/frontier-models/deepseek-v4-flash-api-public-beta
Index: https://aiintelreport.com/llms.txt · Full text: https://aiintelreport.com/llms-full.txt